Gossip-Based Greedy Gaussian Mixture Learning
نویسندگان
چکیده
It has been recently demonstrated that the classical EM algorithm for learning Gaussian mixture models can be successfully implemented in a decentralized manner by resorting to gossip-based randomized distributed protocols. In this paper we describe a gossip-based implementation of an alternative algorithm for learning Gaussian mixtures in which components are added to the mixture one after another. Our new Greedy Gossip-based Gaussian mixture learning algorithm uses gossip-based parallel search, starting from multiple initial guesses, for finding good components to add to the mixture in each component allocation step. It can be executed on massive networks of small computing devices, converging to a solution exponentially faster than its centralized version, while reaching the same quality of generated models.
منابع مشابه
Estimation de modèles de mélange probabilistes: une proposition pour un fonctionnement réparti et décentralise. (A proposal for decentralized, distributed estimation of probabilistic mixture models)
This thesis deals with the distributed statistical estimation, with its motivation from, and application to, multimedia content-based indexing. Algorithms and data from various contributors would cooperate towards a collective statistical learning. The contribution is a scheme for estimating a multivariate probability density in the case where this density takes the form of a Gaussian mixture m...
متن کاملEfficient Greedy Learning of Gaussian Mixture Models
This article concerns the greedy learning of gaussian mixtures. In the greedy approach, mixture components are inserted into the mixture one after the other. We propose a heuristic for searching for the optimal component to insert. In a randomized manner, a set of candidate new components is generated. For each of these candidates, we find the locally optimal new component and insert it into th...
متن کامل